---
library_name: transformers
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV53
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swinv2-tiny-patch4-window8-256-dmae-humeda-DAV53

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8283
- Accuracy: 0.7045

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 6    | 1.6493          | 0.0909   |
| No log        | 2.0   | 12   | 1.5699          | 0.3864   |
| No log        | 3.0   | 18   | 1.4384          | 0.4205   |
| No log        | 4.0   | 24   | 1.2748          | 0.4091   |
| No log        | 5.0   | 30   | 1.2428          | 0.5114   |
| No log        | 6.0   | 36   | 1.0682          | 0.6023   |
| No log        | 7.0   | 42   | 1.2919          | 0.5      |
| No log        | 8.0   | 48   | 0.9125          | 0.6591   |
| No log        | 9.0   | 54   | 1.0308          | 0.5568   |
| No log        | 10.0  | 60   | 0.8505          | 0.6705   |
| No log        | 11.0  | 66   | 0.9354          | 0.625    |
| No log        | 12.0  | 72   | 0.8283          | 0.7045   |
| No log        | 13.0  | 78   | 0.8508          | 0.6705   |
| No log        | 14.0  | 84   | 0.8072          | 0.6477   |
| No log        | 15.0  | 90   | 0.8574          | 0.6477   |
| No log        | 16.0  | 96   | 0.8278          | 0.625    |
| 0.7213        | 17.0  | 102  | 0.8671          | 0.6364   |
| 0.7213        | 18.0  | 108  | 0.8787          | 0.6364   |
| 0.7213        | 19.0  | 114  | 0.8215          | 0.6818   |
| 0.7213        | 20.0  | 120  | 0.8018          | 0.6932   |
| 0.7213        | 21.0  | 126  | 0.8278          | 0.6477   |
| 0.7213        | 22.0  | 132  | 0.8424          | 0.6364   |
| 0.7213        | 23.0  | 138  | 0.8392          | 0.625    |
| 0.7213        | 24.0  | 144  | 0.8371          | 0.625    |
| 0.7213        | 25.0  | 150  | 0.8373          | 0.625    |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0